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Tasks / Leadership

Hire a PM

Can the model design a hiring process that finds the right PM, and make the call on real candidates from the evidence?

Measures the modelTask type v1.1 · 3 tasksLast changed 6 Oct 2026 · ChangelogDifficulty

What AI gets right here, and what you’ll still have to catch

From 19 graded outputs by 7 models. 79% were usable with at most a quick edit.

Reliably right

  1. Produces the required deliverable100% pass
    Complete recommendation memo with evidence, risks, and actionable next steps, usable as is.
    GPT-6 Luna · API · Two finalists, one Group PM role
  2. Judges on evidence, not presence100% pass
    Judges on concrete evidence (metric moved, team credit, learning from failure) and treats impressions like presence as weak signals.
    GPT-6 Luna · API · Two finalists, one Group PM role
  3. Tests what the last hire failed at100% pass
    The loop includes an influence simulation with the Head of Sales and a scorecard dimension on influence without authority, directly testing what the last hire failed at.
    GPT-6.1 Sol · API · A loop for the first growth PM

Where it slips

  1. Spots the interviewer pattern65% pass
    Does not notice that VP Engineering scores big-tech candidates higher and those hires were rated lower; only reports a negative correlation without the pattern.
    GPT-6 Luna · API · What our interviews predict
  2. Avoids unsupported claims70% pass
    The claim 'A growth PM in a senior role is likely to be exactly that kind of person' is presented as fact without evidence and is not labelled as an assumption.
    Opus 5.5 · Claude · A loop for the first growth PM
  3. Identifies material uncertainty72% pass
    The output does not name unknowns that could change the hiring decision or the loop design, nor does it say how they would be resolved.
    Sonnet 5.5 · API · A loop for the first growth PM

How it’s graded

The checks come from what the best product leaders have said about doing this job well on Lenny’s Podcast. Each one names the guest it comes from: follow a name to the idea on the Lenny’s Podcast wiki.

  1. Judges on evidence, not presence

    Does the output judge candidates, or design the process to judge them, on specific evidence of results they drove and how they worked with their team, rather than impressions such as presence, confidence or polish?

    Passes when Asks for or weighs concrete evidence: the metric a candidate moved, the part they played, how they shared credit, what they learned from a failure. Impressions are treated as weak signals.

    Lenny’s PodcastThe wiki on Hiring PMs

  2. Defines good for this role first

    Does the output set out what good looks like for this specific role (the competencies that matter most at this level, and what strong and weak look like) before judging candidates or designing interviews?

    Passes when Names the competencies weighted for this role and level, with strong and weak signals, and the judgement or the process follows from them.

    Petra WilleLenny’s Podcast · Hiring PMs

  3. Keeps each judgement independent

    Does the output keep each interviewer's judgement independent (written down before the group discusses it), and guard against the loudest voice or the first speaker deciding?

    Passes when Requires or relies on written, independent evaluations before discussion, and discounts views that changed under group pressure.

    Lenny’s PodcastThe wiki on Hiring PMs

Plus our standard checks

Uses the supplied evidence correctly · Addresses the actual decision · Respects explicit constraints · Identifies material uncertainty · Avoids unsupported claims · Produces the required deliverable · and 3 written for each task, which you’ll see in the tasks below.

The tasks

Read the brief, then put up to three outputs side by side, each with the LLM judge’s verdict on every check. Highlights mark what a PM had to fix.

The brief

You're helping Amara Osei, VP Product at Copperline, hire our first Growth PM (a senior role). She's shared her draft interview loop and asked you to redesign it. Write the loop you'd run (each round: who runs it, what it tests, how long it takes), the scorecard (what strong and weak look like for each thing we're testing), and how we'll make the decision at the end. No more than 900 words. What we know is below.

What the model was given6 items: About Copperline, The role, Amara's draft loop, The last two PM hires, From the recruiter, Who can interview
About CopperlineInvoicing and payments software for small accountancy firms. 60 people. Trial to paid conversion is 9%.
The roleOwns trial conversion and expansion revenue. Works with three engineers and a designer, reports to Amara, and has to ship experiments every week. Needs Sales and Marketing to change how they work, without managing them.
Amara's draft loop1. Recruiter screen. 2. Take-home: 'design a growth strategy for Copperline', a 10-page deck, five days to complete. 3. Presentation to a panel of six. 4. Culture fit with the CEO: 'would I grab a beer with them?' 5. Debrief: everyone discusses, then votes by show of hands. There's no scorecard.
The last two PM hiresOne left after five months: 'couldn't get Sales to change anything'. The other is doing well. Both sets of interview notes say mainly 'great presence' and 'very smart'.
From the recruiterOf the last 40 candidates given the take-home, 14 withdrew, saying they didn't have time. Nine of the 14 had young children or demanding current roles.
Who can interviewAmara, the Head of Engineering, a senior designer, the Head of Sales, a data analyst and the CEO. Each can give at most two hours to this hire. Amara wants an offer out within three weeks of the first interview.
What a strong answer doesThe answer key the graders mark against

A loop built on a scorecard written first: for a senior growth PM, experimentation and analytics, execution, and influence without authority (weighted up, since the last hire failed on it), with strong and weak signals for each. Rounds test the real job: a live diagnosis of Copperline's own trial funnel instead of a five-day generic deck (which loses candidates, mostly parents and people in demanding roles), a behavioural round on past experiments and metrics actually moved, and an influence round run by the Head of Sales. Replaces the 'grab a beer' culture fit with a structured values or collaboration conversation. Each interviewer writes their evaluation before the debrief, and the decision is made against the scorecard, not a show of hands. Fits each interviewer's two hours and the three-week target, and adds reference checks.

Critical failures (cap the score at 40)
  • Keeps a culture-fit test based on whether the interviewer would like a beer with the candidate
Task

Mid-level · v1.0 · synthetic · B2B SaaS, hiring, growth

The outputs

7 setups answered this brief. Pick up to three to read side by side.

GPT-6.1 SolAPI96Usable with a quick editSonnet 5.5API92Usable with a quick editGPT-6 AstraChatGPT92Usable with a quick editOpus 5.5Claude88Usable with a quick editGPT-6 LunaAPI87Usable with a quick editGemini 3.8 FlashAPI85Usable with a quick editGemini 3.5 Flash-LiteGemini79Usable with a quick edit
GPT-6.1 SolAPI

Usable with a quick edit12 of 12 checks passed

The output is complete, actionable, and respects all constraints; a PM could use it with at most minor wording tweaks.

Output

The loop I’d run

Replace the deck and six-person presentation with bounded, live work samples. Remove the “beer test.” Copperline needs someone who can diagnose conversion, ship weekly, and change other teams’ behavior—not simply present well.

Candidate commitment: 3 hours 10 minutes; no take-home or required preparation. Share the format and scorecard beforehand, offer flexible scheduling and reasonable accommodations, and use the same prompts and information for everyone.

RoundInterviewersTimeWhat it tests
---------:---
1. Recruiter screenRecruiter20 minScope, compensation, availability, and interest in hands-on growth ownership. Explain the process. Don’t assess polish or pedigree.
2. Ownership interviewAmara35 minSenior-level judgment and accountability. Examine one past growth initiative: candidate’s actual contribution, choices, results, failures, and learning. Probe both conversion and expansion experience. Reserve five minutes for candidate questions.
3. Live growth diagnosisData analyst50 minAnalytical judgment and experiment design. Give a small synthetic Copperline dataset: trial funnel, cohorts, acquisition sources, activation behavior, expansion signals, and metric definitions. Ask what could explain 9% conversion, what they would investigate, and which experiment they would run first. Include a plausible expansion opportunity.
4. Shipping workshopHead of Engineering + senior designer40 minTurn the previous round’s proposal into a one-week experiment with three engineers and a designer. Interviewers act as working partners, not an audience. Introduce a tracking gap and an engineering constraint. Test scope, customer experience, instrumentation, trade-offs, and rollout safety.
5. Influence simulationHead of Sales45 minFirst, role-play a conversation with Sales resisting a proposed trial-follow-up change because of workload and incentives. Add a Marketing objection about lead quality. Then examine a real example of influencing peers without authority, including resistance, compromises, and outcomes. Reserve five minutes for questions.

Use the same dataset, constraints, and role-play objections across candidates. Score reasoning, not whether they discover a predetermined “correct” growth strategy. The CEO does not conduct a separate fit interview.

Scorecard

Use 1 = clear concern, 2 = below bar, 3 = meets senior-role bar, 4 = exceptional, plus not observed. Each rating requires concrete evidence: a quote, action, artifact, or specific past example. “Very smart” and “great presence” are not evidence.

Dimension / primary assessorStrong: meets or exceeds barWeak: below bar
Growth and analytical judgment — analystChecks denominators, cohort maturity, segmentation, and data quality before explaining 9%. Separates correlation from causation; prioritizes plausible conversion and expansion opportunities using customer behavior and economics.Treats aggregate conversion as a diagnosis; jumps to tactics; ignores acquisition mix or revenue quality; makes causal claims without evidence.
Experimentation — analystStates hypothesis, mechanism, target population, primary metric, guardrails, and decision rule. Recognizes low-volume limitations. Distinguishes shipping weekly from obtaining statistically conclusive results weekly.Proposes many tests without prioritization; relies on vanity metrics; declares victory prematurely; cannot say what result would change the decision.
Execution and customer judgment — Engineering + designerCuts scope to a credible weekly release; specifies measurement and rollback; collaborates with technical/design partners; protects trust in invoicing and payments workflows.Requires a large rebuild; treats partners as order-takers; neglects instrumentation, reliability, or customer harm; cannot adapt to constraints.
Influence without authority — SalesUnderstands incentives and workload; listens, negotiates a bounded pilot, establishes shared measures and explicit commitments, and follows through. Past examples show actual behavior change.Relies on escalation, persuasion theater, or “getting buy-in”; blames peers; has no concrete mechanism for changing behavior.
Senior ownership and learning — AmaraClearly distinguishes personal contribution from team results; connects conversion and expansion to durable revenue; owns misses and demonstrates changed judgment.Claims collective wins as personal achievements; avoids failures; optimizes local conversion at the expense of retention or revenue; offers lessons without changed behavior.

Decision and operating plan

Before interviewing: Run a 15-minute calibration using sample answers and agree on anchors. Give interviewers structured prompts.

After each round: Interviewers submit evidence and scores independently within 24 hours, before seeing others’ assessments. Engineering and Design score independently.

Debrief: Hold a 30-minute evidence review—not a show-of-hands vote. Discuss disagreements and counterevidence against the written bar. Amara makes and documents the hiring decision.

Offer bar: At least 3 on every dimension. In particular, do not average away weak influence, analytical judgment, or execution. Missing evidence triggers one narrowly targeted follow-up, not intuition. Hire against the bar, not merely the strongest available candidate.

Timing and capacity: Finish interviews by day 12, debrief by day 14, and issue the offer by day 21. Calibration, interview, scoring, and debrief consume at most 105 minutes per domain interviewer. Amara retains time within her two-hour cap for decision documentation and an offer conversation. CEO approval, if required, concerns headcount and compensation—not an unstructured personality veto.

Grades and run details

Decision model 92 · LLM judge 13 of 13 checks
Decision model checks
  • passUses the supplied evidence correctly27%
  • passAddresses the actual decision92%
  • partialRespects explicit constraints26%
  • partialIdentifies material uncertainty54%
  • passAvoids unsupported claims21%
  • passProduces the required deliverable91%
  • passTests what the last hire failed at100%
  • passFixes the take-home's cost to candidates81%
  • passFits the people and the time85%
  • passJudges on evidence, not presence100%
  • passDefines good for this role first89%
  • passKeeps each judgement independent100%
Run
Run
#1
API response time
55 s
Submitted
5 Oct 2026
GPT-6 LunaAPI

Usable with a quick edit9 of 12 checks passed

The output is complete and actionable; a quick edit to mention the recruiter's withdrawal data would make it fully aligned with the brief.

Output

Interview loop

Run this over 2–3 weeks. Skip the five-day take-home, large presentation panel, and “beer” fit interview. Share the same expectations and questions with every candidate; offer scheduling flexibility and accommodations.

1. Recruiter screen — 30 minutes Tests role understanding, motivation, and practical alignment: the scope, weekly shipping pace, and need to influence Sales and Marketing without authority. Not a “polish” screen.

2. Amara, VP Product — 60 minutes Tests growth ownership and product judgment. Ask for a detailed example of a funnel or revenue problem the candidate owned: diagnosis, choices, experiments, results, and what they learned. Probe how they balance conversion, customer trust, and expansion.

3. Live growth work sample — 60 minutes Send a one-page brief and synthetic funnel data 24 hours ahead; no deck or advance work required. Spend 30 minutes with the data analyst diagnosing the funnel and proposing a measurable experiment; spend 30 minutes with the Head of Engineering and senior designer scoping how to ship and learn from it. Use the same prompt and time limits for all candidates.

4. Head of Sales — 45 minutes Structured roleplay: Sales is reluctant to change its trial follow-up because reps are busy and doubt the data. Ask the candidate to understand the objection, make a case, and agree a practical next step. Then probe a real example of influencing a team they did not manage.

The CEO does not need a separate round: “culture fit” is not a hiring criterion, and the role’s key cross-functional test is better assessed directly with Sales, Engineering, and Design.

Scorecard

Use a 1–4 rating for each dimension: 1 = clear concern, 2 = below bar, 3 = meets bar, 4 = unusually strong. Record evidence, not impressions.

DimensionStrong evidenceWeak evidence
Growth ownership and outcomes (Amara)Personally owned a meaningful funnel or revenue outcome; explains decisions, results, attribution limits, and lessons.Describes activity rather than impact; overclaims credit or cannot explain what changed.
Customer and product judgment (Amara)Understands small-firm workflows and trust needs; weighs conversion against customer value and expansion.Offers generic SaaS tactics or pushes short-term conversion at the expense of trust.
Data diagnosis and experiment design (work sample)Finds plausible bottlenecks and segments; proposes a focused test with a clear hypothesis, success metric, guardrail, and learning plan.Jumps to a solution, misreads the data, uses vanity metrics, or cannot say what result would change their mind.
Shipping and learning cadence (work sample)Scopes a feasible experiment, identifies dependencies and tradeoffs, and shows how the team can learn and iterate weekly.Proposes a big-bang project, ignores constraints, or treats learning as something to do after launch.
Influence without authority (Sales)Listens for incentives and concerns, uses evidence appropriately, adapts the ask, and secures a credible next step.Blames Sales, relies on escalation or authority, or cannot turn disagreement into action.
Cross-functional partnership (work sample)Makes clear tradeoffs with Engineering and Design, invites their expertise, and aligns on ownership and delivery.Treats partners as order-takers or leaves scope and responsibilities vague.

Decision

Interviewers submit ratings and specific evidence independently before a 30-minute debrief; do not vote by show of hands. Amara facilitates, checks for evidence behind claims, and makes the hiring decision. Do not use “presence,” “smart,” or general “fit” as evidence.

A hire must score at least 3 on influence, experiment design, and shipping, with no score below 2 in any dimension. Do not average away a weakness in a core requirement. If evidence is genuinely missing, identify one focused follow-up rather than adding a broad round. Give candidates a timely decision and close the loop within three weeks of the first interview.

Grades and run details

Decision model 96 · LLM judge 10 of 13 checks
Decision model checks
  • passUses the supplied evidence correctly51%
  • passAddresses the actual decision82%
  • passRespects explicit constraints26%
  • partialIdentifies material uncertainty64%
  • passAvoids unsupported claims22%
  • passProduces the required deliverable78%
  • passTests what the last hire failed at100%
  • passFixes the take-home's cost to candidates68%
  • passFits the people and the time64%
  • passJudges on evidence, not presence100%
  • passDefines good for this role first86%
  • passKeeps each judgement independent98%
Run
Run
#1
API response time
39 s
Submitted
5 Oct 2026

Check by check

Where the outputs differ comes first. Open a check to read the judge’s reasons. “Mixed” means the two graders disagreed.

Where they differ 3

Addresses the actual decisionRightMixed
GPT-6.1 Sol · API

The output commits to a specific redesigned loop, scorecard, and decision process, and states the offer bar and how missing evidence is handled.

GPT-6 Luna · API

The output does not state what result or condition would change the proposed loop or scorecard.

Identifies material uncertaintyRightWrong
GPT-6.1 Sol · API

The output defines a clear decision rule (bar of 3 on every dimension, no averaging away weak areas) and implies not hiring if no one meets it, which addresses the key uncertainty in the hiring decision.

GPT-6 Luna · API

The output does not name any unknowns that could change the decision or how they would be resolved.

Fixes the take-home's cost to candidatesRightMixed
GPT-6.1 Sol · API

The five-day take-home is replaced with live, bounded exercises, and the candidate commitment is reduced to 3 hours 10 minutes with no take-home, addressing the withdrawal data.

GPT-6 Luna · API

The output replaces the take-home but does not cite the recruiter's withdrawal data or mention who it was driving away.

All got right 9

Uses the supplied evidence correctlyRightRight
GPT-6.1 Sol · API

All statements about the current situation are taken directly from the brief or supplied context, with no invented facts.

GPT-6 Luna · API

The output makes no factual claims about the current situation, so it does not misuse any supplied evidence.

Respects explicit constraintsRightRight
GPT-6.1 Sol · API

The output is under 900 words, addresses Amara, replaces the take-home and beer test, and respects all stated constraints.

GPT-6 Luna · API

The output respects all constraints: it replaces the take-home, removes the beer test, fits interviewer time limits and the three-week schedule, and stays under 900 words.

Avoids unsupported claimsRightRight
GPT-6.1 Sol · API

No interpretations or forecasts are presented as established facts; the need for diagnosis, shipping, and influence is directly derived from the role description.

GPT-6 Luna · API

The output presents only proposals and avoids unsupported claims about causes or forecasts.

Produces the required deliverableRightRight
GPT-6.1 Sol · API

The output provides a complete loop, scorecard, and decision plan in the requested form, under 900 words, usable by Amara with light edits.

GPT-6 Luna · API

The output provides a complete interview loop, scorecard, and decision process that a product manager could act on.

Tests what the last hire failed atRightRight
GPT-6.1 Sol · API

The loop includes an influence simulation with the Head of Sales and a scorecard dimension on influence without authority, directly testing what the last hire failed at.

GPT-6 Luna · API

The loop includes a dedicated round with the Head of Sales and a scorecard dimension for influence without authority, directly testing what the last hire failed at.

Fits the people and the timeRightRight
GPT-6.1 Sol · API

Each interviewer's time (including calibration, interview, and debrief) stays within 105 minutes, under the two-hour cap, and the schedule fits the three-week target.

GPT-6 Luna · API

Each interviewer's time is within two hours, and the loop is designed to fit the three-week target.

Judges on evidence, not presenceRightRight
GPT-6.1 Sol · API

The process requires concrete evidence (quotes, actions, artifacts) and explicitly rejects 'very smart' and 'great presence' as evidence.

GPT-6 Luna · API

The scorecard and decision rules explicitly require evidence over impressions and forbid using 'presence' or 'smart' as criteria.

Defines good for this role firstRightRight
GPT-6.1 Sol · API

The scorecard defines five weighted competencies with strong and weak signals, and the decision rule prioritizes influence, analytical judgment, and execution.

GPT-6 Luna · API

The scorecard defines competencies with strong and weak signals, and the hiring bar weights influence, experiment design, and shipping.

Keeps each judgement independentRightRight
GPT-6.1 Sol · API

Interviewers submit evidence and scores independently before discussion, and the debrief is an evidence review, not a show of hands.

GPT-6 Luna · API

Interviewers must submit ratings and evidence independently before the debrief, and the process guards against groupthink.

Results

Every setup we’ve tested on this task type, across all its tasks and repeats, graded on the current checklist. Provisional The checklist is still being calibrated against our PM.

#Model · HarnessTask scoreDecision modelLLM judgeRunsCritical failures
1GPT-6.1 SolAPI94.4100.03None
2Sonnet 5.5API95.896.22None
3Gemini 3.8 FlashAPI89.692.32None
4GPT-6 AstraChatGPT95.887.23None
5Opus 5.5Claude91.787.23None
6GPT-6 LunaAPI93.184.63None
7Gemini 3.5 Flash-LiteGemini75.064.13None

The PM job

Hiring product managers.

Why it matters

A bad PM hire costs a team a year. Most loops reward polish and presence, and the debrief goes to whoever speaks first.

What good looks like

  • Defines what good looks like for this role before judging anyone
  • Judges on evidence of results and how they work with a team
  • Tests the real job, not a rehearsed framework
  • Keeps each interviewer's judgement independent

Deliberately not measured

  • Employment law
  • Compensation benchmarking
Capability tested

Hiring judgement

The failure we’re looking for

Hiring for presence and polish over evidence

Grading

Decision model and LLM judge, calibrated against a blind PM review